In general, such an analysis can be conducted under either parametric or nonparametric settings. Parametric approaches rely heavily upon assumptions of underlying distributions belonging to a known family. Likelihood or penalized likelihood functions are generally involved ([1]}; [2]}; [3]}). In contrast, nonparametric approaches make very few assumptions regarding stochasticity underlying the time series. The likelihood principle is not directly applicable. Nonetheless, such approaches fit well in a wider variety of applications. Such an advantageous feature has popularity and a vast amount of research attention in the past decade.
